in today’s paper Moosavi-Dezfooli et al., show us how to create a _single_ perturbation that causes the vast majority of input images to be misclassified.

So then they reach inside to one of the layers and spin the knob randomly to fuck it up. Lower layers are edges and curves. Higher layers are faces, eyes and shoggoth ovipositors. [....] But the best part is not when they just glitch an image -- which is a fun kind of embossing at one end, and the "extra eyes" filter at the other -- but is when they take a net trained on some particular set of objects and feed it static, then zoom in, and feed the output back in repeatedly. That's when you converge upon the platonic ideal of those objects, which -- it turns out -- tend to be Giger nightmare landscapes. Who knew. (I knew.)